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All Journal Jurnal Pekommas Al Tijarah Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control ILKOM Jurnal Ilmiah Jurnal Surya Masyarakat JURNAL NUSANTARA APLIKASI MANAJEMEN BISNIS JURIKOM (Jurnal Riset Komputer) Ensiklopedia of Journal JUPE : Jurnal Pendidikan Mandala Abdimas Universal Jurnal Ilmu Pendidikan Islam Jurnal Surya Masyarakat Journal of Intelligent Computing and Health Informatics (JICHI) INDRA: Jurnal Pengabdian kepada Masyarakat JURNAL MUARA PENDIDIKAN Jurnal Ilmu Pendidikan dan Sains Islam Interdisipliner Journal of Community Empowerment Jurnal Social Society
Ilham, Ahmad
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Religion Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Klasifikasi Kunyit dan Temulawak dengan VGG16 dan Fuzzy Tsukamoto Berbasis Android Setyawan, Muhammad Rizki; Bahari Putra, Fajar Rahardika; Ilham, Ahmad; Suseno, Dimas Adi
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8696

Abstract

Indonesia has a very rich biodiversity, including various medicinal plants that are highly financially beneficial and health-promoting. Among these medicinal plants, temulawak and turmeric are the two most popular rhizomes widely used in traditional medicine as well as the herbal industry. However, because the shape and color of these two plants are very similar, it is often difficult to distinguish between them, especially for laypeople and new industry workers. This research developed an Android-based application that can effectively and accurately distinguish between temulawak and turmeric to address this issue. For this application, the Convolutional Neural Network (CNN) architecture of the VGG-16 model is used along with the Tsukamoto fuzzy method as an additional layer. The trials conducted on the developed model using test data showed an accuracy rate of 0.97, a recall value of 0.98, and an F1 score of 0.97. Meanwhile, the blackbox testing shows that this application functions stably without technical issues, making it ready for use. Additionally, blackbox testing shows that the system can function stably without any issues, making it suitable for real-world use
Detection of Curcuma and Turmeric Differences Utilizing Fuzzy Tsukamoto Android-Based CCN Model Putra, Fajar Rahardika Bahari; Setyawan, Muhammad Rizki; Ilham, Ahmad; Suseno, Dimas Adi
ILKOM Jurnal Ilmiah Vol 17, No 3 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i3.2857.276-291

Abstract

Turmeric and curcuma are herbs that are often used in medicine and cooking. However, their similar shapes and colours make it difficult for people, especially in Southwest Papua, to distinguish between them directly. According to the Central Statistics Agency (BPS) in 2023, turmeric production reached 18,302 units, far higher than turmeric, which only reached 2,950 units. Based on field interviews in Southwest Papua, more than 60% of respondents had difficulty distinguishing turmeric from turmeric. To address this issue, this research develops an Android-based classification system by integrating the Fuzzy Tsukamoto algorithm with Convolutional Neural Network (CNN) models. Five CNN models VGG16, MobileNetV2, NASNetMobile, EfficientNetB2, and EfficientNetB3 were selected based on their balance between computational efficiency (MobileNetV2, NASNetMobile), depth and proven stability (VGG16), and modern scalable architectures (EfficientNetB2 and B3). Each model was combined with fuzzy logic to enhance classification accuracy. he dataset consisted of 800 images of curcuma and turmeric obtained from Kaggle and field collections. The data were divided into training, validation, and testing sets, and augmented through a series of transformations including rescaling to a range of 0 to 1, rotation up to 40 degrees, horizontal shift of 20%, angular distortion (shear) of 20%, zoom up to 30%, horizontal flipping, and brightness adjustment. Empty areas generated during augmentation were filled using the nearest pixel value with the ‘nearest’ mode to preserve image integrity. Training was performed using the AdamW optimizer and fine-tuning. Model evaluation employed accuracy, precision, recall, F1-score, and confusion matrix metrics. The results showed that the VGG16 model performed best, achieving 97% accuracy, 98% precision, 97% recall, and 98% F1-score, as confirmed by the classification report and confusion matrix. This model was also the most stable when tested on the Android system, while EfficientNetB2 and B3 produced less satisfactory outcomes. These findings demonstrate that combining CNN and Fuzzy Tsukamoto improves the classification accuracy of images with high visual similarity. The proposed system has the potential to be applied as a direct plant identification tool in the field and can be further extended to classify other visually similar plants
Inovasi Sosial Tubanan Agrocyrcleforestry: Sebuah Studi Menggunakan Metode Social Return On Investment (SROI) A. Khoirul Anam; Arifin, Miftah; Mahaputra, Wahyu; Ilham, Ahmad
Jurnal Nusantara Aplikasi Manajemen Bisnis Vol 8 No 2 (2023): Jurnal Nusantara Aplikasi Manajemen Bisnis
Publisher : UNIVERSITAS NUSANTARA PGRI KEDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/nusamba.v8i2.19903

Abstract

Research aim: The study aims to assess the value of the impact of PT PLN UIK TJB's CSR program on the implementation of social innovation in Tubanan agroforestry. In this approach, the effect of the program has an essential meaning for the beneficiaries of the program, namely the farming community group in Tubanan Village. Design/Methode/Approach: This study used social return on investment (SROI) as a research methodology. This research was conducted on the beneficiaries of the Tubanan agroforestry program and considered all stakeholders directly or indirectly involved in the program. The research informants numbered 20 people who were members of the LMDH Tunas Agung in Tubanan Village. Research Finding: The results showed that the CSR programs generated social benefits on investment and provided economic, social, and environmental benefits. SROI as a solution that changes the mindset of investment analysis based on outcomes is not just output. Theoretical contribution/Originality: This study allows us to expand the evidence of the critical role of social innovation for farming community groups, but so far, little has been studied about the application of SROI as an assessment methodology. Practitionel/Policy implication: The results of the SROI analysis become the basis for improving the planning of subsequent CSR programs. Research limitation: The selection of financial results and the proxies used are potentially biased, even though the proxies have been quantified over a potential range, the impact value has been reduced by filters (deadweigh, attribution, displacement, drop-off), and only emphasizes the impact on hard results rather than soft ones, which are considered less valuable.
Co-Authors A. Khoirul Anam A. Octamaya Tenri Awaru Abdollahi, Jafar Abdul Nizar Adi Nugroho Adilla, Nia Adinullhaq, Juyus Muhammad Agatra, Denaya Ferrari Noval Ahmad Ahmad Farhan, Ahmad Ahyana, Afan Arga Aini, Isna Nur Akhmad Fathurohman Akhmad Fathurrohman Al Malik, M. Warisa Alfiana, Elsa Wahyu Amal Witonohadi Amylia, Aura Anam, A Khoirul Andi Aco Agus, Andi Aco Anggana, Muhammad Wahyu April Liana, Dhewi Apriliah, Mifta Apriyanto, Riki Ardhani, Yevi Alviatul Ariyanto, Nova Bahari Putra, Fajar Rahardika Bayu Kristianto Cornella, Barisma Ami Dewi Citrawati Dhendra Marutho Disma, Amanda Fatma Putri Dwi Setia Anugrah, Muhamad Fadli Emelia Sari Erwin Budianto Estuhono, Estuhono Fadilatul Fajriyah, Rizqi Febrianto Febrianto, Febrianto Firmasyah, Teguh Fitri Ayuning Tyas Habyba, Anik Nur Herlyana, Yuniar Iveline Anne Marie Kahar, Muhammad Syahrul Kahayani, Zahra Kamaruddin, Syamsu A Khatimah, Andi Weyana Nurul Khomsiana, Yeni Aqnes Khumairah, Tuffahati Sahna Khusna, Meisya Maulida Kindarto, Asdani Koli, Yulenni Bandora Kurnia, Janu Yogi Lorenza, Diana Lukman Assaffat Luqman Assaffat Mahaputra, Wahyu Maharani, Anisya Maulida, Nur Khilya Miftah Arifin Muhamad, Farezki Muhammad Firmansyah, Muhammad Muhammad Munsarif Muhammad Rizki Setyawan Muhammad Sam'an Muhammad Taufiqurrahman, Muhammad Munsarif, Muhammad Muza'in, Muhammad Muzayyanah, Ulfatul Elsa Nabila, Shadrina Putri Najamuddin Najamuddin, Najamuddin Natalia, Devitri Ni'am, Falahun Novia, Syakila Ana Sajidah Putri Noviandi Noviandi, Noviandi Nur, Muhammad Adiv Anas Nurmantoro, Irvan Parwadi Moengin Putra, Fajar Rahardika Bahari Putri, Berliana Qori’nurrahman, Faqihana Ananda Ramadhani, Arfido Ramadhani, Rima Dias Ramea Astri, Tita Riski Amaliah, Riski Rizki Jayanti, Dian Safuan Safuan Sam’an, Muhammad Sangadji, Zulkarnain Saputra, Irwansyah Saputra, Tegar Sasmita, Nanda Yulia Setia Iriyanto Setianama, Mamur Setyaningsih, Ayu Sholakhudin, Akhmad Sundari Sundari Suryana, Yunita Friscilia Suseno, Dimas Adi Sutarno Sutarno Syafitiri, Urzha Dian Syaifani, M. Amin Trianita, Nisa Adelia Ulfa, Helya Cholifatul Ulinuha, Mohammad Wulan Cahya Ningrum